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Related Concept Videos

Combinatorial Gene Control02:33

Combinatorial Gene Control

Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the addition of a...
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Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
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Constitutive and Regulated Gene Expression

Gene expression in prokaryotes is governed by constitutive and regulated systems, allowing cells to balance the production of essential proteins with adaptive responses to environmental changes.Constitutive Gene ExpressionConstitutive, or housekeeping, genes are continuously expressed as they encode proteins vital for fundamental cellular processes. These include enzymes for glycolysis, ribosomal components for protein synthesis, and proteins involved in DNA replication. Their constant...
Gene Regulation in Microbial Communities: Quorum Sensing01:28

Gene Regulation in Microbial Communities: Quorum Sensing

Quorum sensing is a mechanism of bacterial communication that enables coordinated gene expression in response to changes in population density. This facilitates collective behaviors that enhance survival, resource acquisition, and ecological adaptation. This process relies on small signaling molecules called autoinducers that accumulate as bacterial populations grow. When a critical threshold concentration of autoinducers is reached, bacterial cells collectively modify gene expression,...

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A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells
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Integrating quantitative knowledge into a qualitative gene regulatory network.

Jérémie Bourdon1, Damien Eveillard, Anne Siegel

  • 1Computational Biology (ComBi) Group, LINA UMR 6241, Université de Nantes, Ecole des Mines de Nantes & CNRS, Nantes, France. Jeremie.Bourdon@univ-nantes.fr

Plos Computational Biology
|September 22, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a probabilistic model integrating qualitative gene interactions and quantitative protein data. The novel framework accurately predicts protein concentration changes and ranks interaction importance in biological systems.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Molecular Systems Biology

Background:

  • Biological knowledge is incomplete, necessitating models using partial information.
  • Current research often focuses on qualitative network behaviors, neglecting quantitative data like protein concentration dynamics.
  • Integrating diverse data types is crucial for a comprehensive understanding of living systems.

Purpose of the Study:

  • To develop a probabilistic modeling framework that integrates both qualitative and quantitative biological information.
  • To accurately predict time-series protein concentration variations using limited data.
  • To quantify the importance of gene interactions within a biological network.

Main Methods:

  • Utilized average case analysis combined with Markov chains.
  • Linked qualitative transcriptional regulation information to quantitative protein concentrations.
  • Modeled the carbon starvation response in Escherichia coli.

Main Results:

  • Accurately predicted quantitative time-series evolution of several protein concentrations.
  • Required only discrete gene interaction knowledge and minimal quantitative observations.
  • Derived a literature-confirmed ranking of interaction importance.

Conclusions:

  • The developed hybrid model successfully integrates qualitative and quantitative data, even with limited quantitative information.
  • The method enables novel quantitative predictions and precise quantification of interaction relevance.
  • The approach provides insights for experimental design by identifying key model features.